Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "61" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 43 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 41 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459858 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.373879 | 2.284263 | -1.884060 | -1.070354 | 6.897919 | -2.117400 | 0.449537 | 3.540206 | 0.6863 | 0.6438 | 0.4088 | 2.807099 | 2.468817 |
| 2459857 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.003943 | 16.490695 | 8.718970 | 9.311253 | 125.802671 | 136.323677 | 36.929476 | 49.116420 | 0.6584 | 0.6582 | 0.4237 | nan | nan |
| 2459856 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.862774 | 4.436167 | 1.603233 | 1.781822 | 6.894646 | 1.456078 | -0.257261 | 3.185041 | 0.6700 | 0.6611 | 0.3941 | 2.551509 | 2.398184 |
| 2459855 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 5.553506 | 5.078359 | 3.171309 | 2.191720 | 6.036599 | 0.646494 | -0.285103 | 1.710563 | 0.6462 | 0.6656 | 0.4288 | 2.815549 | 2.590845 |
| 2459854 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 5.651783 | 5.987536 | 3.017570 | 2.698383 | 4.041630 | 0.423704 | -0.212315 | 3.097679 | 0.6769 | 0.6961 | 0.4235 | 2.808245 | 2.469144 |
| 2459853 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 290.535673 | 290.838314 | inf | inf | 3971.304384 | 3900.022268 | 12485.943686 | 12542.449101 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459852 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.344621 | 5.062153 | 5.176110 | 2.571699 | 2.589578 | 4.452413 | 4.847119 | 2.846617 | 0.8079 | 0.8044 | 0.2492 | 4.300196 | 4.053990 |
| 2459851 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.425573 | 6.279957 | 4.700033 | 3.285856 | 2.174546 | 9.536356 | 1.750455 | 6.402465 | 0.7136 | 0.7056 | 0.3406 | 2.618152 | 2.314256 |
| 2459850 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.238006 | 5.765650 | 3.432056 | 3.005442 | 3.995943 | 4.160238 | 0.349184 | 6.259522 | 0.6984 | 0.7140 | 0.3440 | 2.767619 | 2.544806 |
| 2459849 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 5.105155 | 5.176152 | 5.752532 | 7.186482 | 8.353061 | 2.201440 | -0.100183 | 6.148903 | 0.6915 | 0.7091 | 0.3571 | 3.037167 | 2.931871 |
| 2459848 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.414566 | 4.561601 | 7.831275 | 7.589794 | 8.336093 | 4.181156 | -0.266730 | 5.743907 | 0.6871 | 0.7191 | 0.3729 | 2.132307 | 2.093209 |
| 2459847 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.555080 | 5.026878 | 9.245020 | 7.136333 | 8.490270 | 4.206512 | 0.092355 | 1.985801 | 0.6945 | 0.6468 | 0.4261 | 5.533443 | 5.205221 |
| 2459846 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.469017 | 4.914879 | 6.722091 | 5.456769 | 3.384881 | 3.444247 | 1.239081 | 4.490981 | 0.8255 | 0.6488 | 0.5132 | 3.790749 | 3.444551 |
| 2459845 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 5.558595 | 5.730449 | 9.085153 | 10.747069 | 13.078277 | 5.707816 | 7.976880 | 14.806757 | 0.7158 | 0.7291 | 0.3619 | 0.000000 | 0.000000 |
| 2459844 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 35.789226 | 41.020599 | 113.843368 | 122.949932 | 283.090025 | 202.410135 | 103.821198 | 108.766232 | 0.8677 | 0.6230 | 0.5817 | nan | nan |
| 2459843 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.529349 | 3.089329 | 11.040154 | 12.503875 | 74.334021 | 74.449396 | 2.465296 | 7.199451 | 0.7546 | 0.7538 | 0.3856 | 5.966480 | 4.796899 |
| 2459842 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.307012 | 0.259584 | -3.066710 | -2.866826 | -0.574456 | -2.564679 | -0.750648 | 0.814254 | 0.7464 | 0.6574 | 0.2666 | 4.412045 | 3.407379 |
| 2459841 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 33.801395 | 36.439475 | 94.739649 | 99.610490 | 362.335033 | 265.834965 | 27.049059 | 39.785851 | 0.7476 | 0.7239 | 0.3806 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 6.897919 | 2.284263 | 2.373879 | -1.070354 | -1.884060 | -2.117400 | 6.897919 | 3.540206 | 0.449537 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Variability | 136.323677 | 16.490695 | 15.003943 | 9.311253 | 8.718970 | 136.323677 | 125.802671 | 49.116420 | 36.929476 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 6.894646 | 4.862774 | 4.436167 | 1.603233 | 1.781822 | 6.894646 | 1.456078 | -0.257261 | 3.185041 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 6.036599 | 5.078359 | 5.553506 | 2.191720 | 3.171309 | 0.646494 | 6.036599 | 1.710563 | -0.285103 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Shape | 5.987536 | 5.987536 | 5.651783 | 2.698383 | 3.017570 | 0.423704 | 4.041630 | 3.097679 | -0.212315 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Power | inf | 290.838314 | 290.535673 | inf | inf | 3900.022268 | 3971.304384 | 12542.449101 | 12485.943686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Power | 5.176110 | 3.344621 | 5.062153 | 5.176110 | 2.571699 | 2.589578 | 4.452413 | 4.847119 | 2.846617 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Variability | 9.536356 | 3.425573 | 6.279957 | 4.700033 | 3.285856 | 2.174546 | 9.536356 | 1.750455 | 6.402465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Discontinuties | 6.259522 | 4.238006 | 5.765650 | 3.432056 | 3.005442 | 3.995943 | 4.160238 | 0.349184 | 6.259522 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 8.353061 | 5.105155 | 5.176152 | 5.752532 | 7.186482 | 8.353061 | 2.201440 | -0.100183 | 6.148903 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 8.336093 | 4.561601 | 4.414566 | 7.589794 | 7.831275 | 4.181156 | 8.336093 | 5.743907 | -0.266730 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Power | 9.245020 | 5.026878 | 4.555080 | 7.136333 | 9.245020 | 4.206512 | 8.490270 | 1.985801 | 0.092355 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Power | 6.722091 | 4.469017 | 4.914879 | 6.722091 | 5.456769 | 3.384881 | 3.444247 | 1.239081 | 4.490981 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Discontinuties | 14.806757 | 5.730449 | 5.558595 | 10.747069 | 9.085153 | 5.707816 | 13.078277 | 14.806757 | 7.976880 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 283.090025 | 35.789226 | 41.020599 | 113.843368 | 122.949932 | 283.090025 | 202.410135 | 103.821198 | 108.766232 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Variability | 74.449396 | 3.089329 | 2.529349 | 12.503875 | 11.040154 | 74.449396 | 74.334021 | 7.199451 | 2.465296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | nn Temporal Discontinuties | 0.814254 | -0.307012 | 0.259584 | -3.066710 | -2.866826 | -0.574456 | -2.564679 | -0.750648 | 0.814254 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | N06 | not_connected | ee Temporal Variability | 362.335033 | 33.801395 | 36.439475 | 94.739649 | 99.610490 | 362.335033 | 265.834965 | 27.049059 | 39.785851 |